Arrangement for evaluating network capacity, network utilization, and network efficiency in a communications network

ABSTRACT

A network having known network parameters is evaluated based on determined network capacity, utilization, and efficiency attributes relative to different traffic constraints and routing constraints applied to the known network parameters. Each of the network capacity, utilization, and efficiency attributes have prescribed definitions based on the network parameters and relative to the different traffic constraints and routing constraints, and are determined based on linear programming computations of the respective prescribed definitions and the network parameters. The network capacity, utilization, and efficiency attributes can then be applied to computer-based optimization resources configured for determining optimum network parameters for the network.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to network design, and traffic analysisincluding evaluation of a communications network, for example abroadband communications network.

2. Description of the Related Art

Existing communications networks include nodes and circuits, alsoreferred to as links, that interconnect the nodes. Certain nodes may beconfigured as source/destination nodes configured forintroducing/outputting data traffic to/from the network; hencesource/destination nodes are considered to “originate” traffic into thenetwork and “sink” traffic from the network. Other nodes are configuredas transit nodes (i.e., intermediate nodes) that only can switchtraffic, and cannot originate or sink traffic.

An owner of a large scale communications network, for example a backbonenetwork configured for transporting interexchange carrier traffic orwide area network traffic, sells or leases network resources by offeringnetwork access to the communications network in terms of a prescribeddata rate, for example a prescribed leased bandwidth or a prescribedleased capacity. The sale or lease of network resources based on aprescribed capacity, for example continuous 10 Gigabit per second (Gbps)capacity, requires that the network owner employ network engineeringduring network deployment to estimate the data-carrying capacity of thelarge scale communications network.

Network design capacity typically is estimated during design of thenetwork based on selection of hardware components and circuits havingprescribed specifications relative to design criteria. For example,network engineers designing a network for deployment will estimate thenetwork capacity based on prescribed specifications of the hardwarecomponents used to implement the nodes, and the bandwidth capacity ofthe circuits interconnecting the nodes. In particular, hardwarecomponents such as ATM switches, Ethernet (IEEE 802.3) switches, framerelay switches, microwave repeaters, satellite ground stations, etc.typically will specify their respective capacities (e.g., 100 Mbpsports, 1 Gbps ports, OC-48 SONET ports, etc.). As the hardwarecomponents and circuits are installed, typically the hardwarecomponents/circuits and their associated attributes (including networkcapacity attributes specified in the prescribed specifications) areadded to a network inventory database, indicating the installed hardwarecomponents are available for network service. The installed hardwarecomponents/circuits are then provisioned for network service, enablingthe installed hardware components/circuits to service network traffic.Hence, network capacity is affected as hardware components/circuits areadded to the network.

The capacity of a large scale communications network in transportingnetwork traffic (i.e., network capacity), however, cannot be preciselydetermined merely by identifying the prescribed capacity specificationsof the installed hardware components. In particular, actual networkcapacity invariably is less than design estimates due to factorsassociated with deployment of the network. Determining network capacitybecomes substantially more complex as the number of circuits and nodesincreases in the network, and as network traffic increases. Hence,network owners encounter increasing difficulty in accurately assessingavailable network capacity for selling or leasing network resources, andfor determining whether network expansion is required.

SUMMARY OF THE INVENTION

There is a need for an arrangement that enables a communications networkto be evaluated accurately and precisely based on determining networkparameters associated with network design, network traffic, and routingconstraints.

There also is a need for an arrangement that enables evaluation ofnetwork capacity, network utilization, and network efficiency in acommunications network.

These and other needs are attained by the present invention, where anetwork having known network parameters is evaluated based on determinednetwork capacity, utilization, and efficiency attributes relative todifferent traffic constraints and routing constraints applied to theknown network parameters. Each of the network capacity, utilization, andefficiency attributes have prescribed definitions based on the networkparameters and relative to the different traffic constraints and routingconstraints, and are determined based on linear programming computationsof the respective prescribed definitions and the network parameters. Thenetwork capacity, utilization, and efficiency attributes can then beapplied to computer-based optimization resources configured fordetermining optimum network parameters for the network.

One aspect of the present invention provides a method of evaluating anetwork having origination/destination nodes and interconnecting linksaccording to a prescribed network topology. The method includesdetermining an absolute capacity of the network, having a determinedtraffic distribution and prescribed routing constraints, based oncapacity attributes for the respective links, the absolute capacityidentifying a maximum traffic volume that can be carried by the networkaccording to the prescribed network topology, independent of thedetermined traffic distribution and the prescribed routing constraints.The method also includes determining a second network capacity based onthe capacity attributes and the determined traffic distribution betweenthe respective origination/destination nodes, the second networkcapacity identifying a maximum traffic volume that can be carried by thenetwork according to the prescribed network topology and the determinedtraffic distribution, independent of the prescribed routing constraints.The determined traffic distribution is evaluated relative to theprescribed network topology based on the absolute capacity and thesecond network capacity.

An additional feature of this aspect of the present invention involvesthird determining a third network capacity based on the capacityattributes, the determined traffic distribution and the prescribedrouting constraints. The third network capacity identifies a maximumtraffic volume that can be carried by the network according to theprescribed network topology, the determined traffic distribution and theprescribed routing constraints. This additional feature also includesincluding, within the evaluating step, the feature of second evaluatingthe prescribed routing constraints relative to the prescribed networktopology and the determined traffic distribution based on the absolutecapacity and the third network capacity.

Additional advantages and novel features of the invention will be setforth in part in the description which follows and in part will becomeapparent to those skilled in the art upon examination of the followingor may be learned by practice of the invention. The advantages of thepresent invention may be realized and attained by means ofinstrumentalities and combinations particularly pointed out in theappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference is made to the attached drawings, wherein elements having thesame reference numeral designations represent like elements throughoutand wherein:

FIGS. 1A, 1B and 1C are diagrams illustrating physical network topology,link capacity, and link traffic, respectively, for a network havingnodes and interconnecting links according to a prescribed networktopology that can be evaluated according to an embodiment of the presentinvention.

FIG. 2 is a diagram of a computer-based system configured for evaluatingthe network of FIG. 1 according to an embodiment of the presentinvention.

FIG. 3 is a diagram illustrating a method of evaluating the network ofFIG. 1 according to an embodiment of the present invention.

FIG. 4 is a diagram illustrating a second network that can be evaluatedaccording to an embodiment of the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION

The disclosed embodiment is directed to an arrangement for evaluatingnetwork attributes based on determined network capacity, utilization,and efficiency attributes relative to different traffic constraints androuting constraints applied to the known network parameters. Inparticular, the disclosed embodiment provides precise definitions forprescribed metrics used to evaluate a network. The prescribed metrics,described below, are determined by utilizing commercially availablelinear programming and mixed integer programming software resources.Once the prescribed metrics have been determined, the network attributescan be evaluated based on comparing the relative values of theprescribed metrics to determine whether the network is operating atcapacity, whether the capacity is being effectively utilized toaccommodate additional traffic growth, and whether the capacity is beingutilized efficiently.

Moreover, since network performance is based on a combination of networkdesign factors including network topology, traffic distribution withinthe network, and routing constraints, the prescribed metrics can besuccessively applied to multiple network design factors in a prescribedhierarchy. Hence, the network design factors can be analyzed todetermine whether a network having a prescribed network topology isutilizing an optimum traffic distribution, and whether prescribedrouting constraints (e.g., hop count, maximum delay interval, averagenetwork delay, etc.) are being utilized efficiently within theprescribed network topology according to a prescribed trafficdistribution.

Hence, network engineers can accurately and precisely evaluate networkperformance based on network topology, network traffic distribution, andidentified routing constraints to determine whether reconfiguration ofthe network is needed to improve efficiency, or increase capacity.

FIGS. 1A, 1B, and 1C are diagrams illustrating different attributes of anetwork 10 having nodes 12 and links 14 interconnecting the nodes. Inparticular, FIG. 1A illustrates physical network topology, FIG. 1Billustrates link capacity, and FIG. 1C illustrates link traffic. Asillustrated in each of the FIGS. 1A, 1B, and 1C, the network 10 includesnodes 12 a, 12 b, 12 c, and 12 d, labeled “1”, “2”, “3”, and “4”,respectively. The nodes 12 a, 12 b, and 12 c are deemedorigination/destination nodes that can supply or output network trafficinto and out from the network 10, and the node 12 d is deemed a transitnode (i.e., an intermediate node) configured solely before switchingnetwork traffic to another network node.

As illustrated in FIG. 1A the nodes 12 are interconnected byinterconnecting links 14, illustrated as two-way links having a two-waybandwidth of 150 Mbps. FIG. 1B illustrates in further detail the linktraffic configuration, where each of the interconnecting links arecomposed of 1-way links 16, each illustrated as having a correspondinglink capacity of 150 Mbps. Hence, the bidirectional circuits 14 aremodeled herein as two unidirectional links 16.

FIG. 1C illustrates the traffic routing, where all traffic is passedbetween nodes 12 a (“1”) and 12 c (“3”). In particular, the trafficrouting includes a traffic component 18 a from node 12 a to node 12 cvia node 12 b; a traffic component 18 b (composed of legs 18 b ₁ and 18b ₂) from node 12 a to node 12 c via node 12 d; a traffic component 18 cfrom node 12 c to node 12 a via node 12 b; and a traffic component 18 d(composed of legs 18 d ₁ and 18 d ₂) from node 12 a to node 12 c vianode 12 d.

According to the disclosed embodiment, network capacity is defined asthe maximum volume of traffic that can enter and leave the network, alsoreferred to as the network's maximum throughput. Network capacity alsocan be measured in bits per second. In particular, three capacitymetrics are used to specify differing types of capacity within thenetwork 10: absolute capacity (C_(A)) of the network 10, capacity(C_(T)) for a given traffic distribution 18, and capacity (C_(TR)) for agiven traffic distribution and prescribed routing constraints.

Absolute capacity (C_(A) is defined as the maximum throughput of thenetwork 10 without any routing constraints or traffic distributionconstraints. Hence, the only constraints used for absolute capacity isthe maximum link utilization constraints, in other words the capacity ofthe individual links 16 associated with the nodes 12, discountingtransit nodes. As illustrated in FIG. 1B, the absolute capacity (C_(A))can be determined to equal 900 Mbps (C_(A)=900 Mbps) (after discountingtransit node 12 d) from the following capacity matrix C that specifiesthe link capacity C_(ij) for each link 16 configured for sending datafrom source node i to destination node j:

$C = \begin{bmatrix}0 & 150 & 0 & 150 \\150 & 0 & 150 & 0 \\0 & 150 & 0 & 150 \\150 & 0 & 150 & 0\end{bmatrix}$The problem of determining the absolute network capacity C_(A) isequivalent to finding a traffic matrix T with the largest possible sumof entries T_(ij). A formal definition that can be used to determineC_(A) using linear programming resources is described in the attachedAppendix A.

Capacity for a Given Traffic Distribution (C_(T)) is defined as themaximum throughput of a network 10 for a given traffic distribution,where traffic distribution is defined as the relative size of trafficbetween different origination/destination pairs. As illustrated in FIG.1C, the capacity for the given traffic distribution (C_(T)), relative tothe traffic components 18, can be determined to equal 600 Mbps(C_(T)=600 Mbps) from the following traffic matrix T that specifies thelink traffic T_(kl) for each link traffic component 18 from source nodek to destination node 1:

$T = \begin{bmatrix}0 & 0 & 300 \\0 & 0 & 0 \\300 & 0 & 0\end{bmatrix}$As described below, the objective in calculating C_(T) is to determinehow much traffic can grow (based on a maximum growth factor α_(max))while conforming to a given traffic distribution without exceedingmaximal allowable utilization on any link. For example, the maximumgrowth factor for the above traffic matrix T is α_(max)=1 due to thelink capacities, indicating no room for growth. However, if the trafficmatrix T was substituted with an alternate traffic matrix T2, then themaximum growth factor α_(max) for the alternate traffic matrix T2 mayalso differ. Hence, if the alternate traffic matrix T2 for the network10 includes the traffic components:

${{T2} = \begin{bmatrix}0 & 100 & 100 \\100 & 0 & 100 \\100 & 100 & 0\end{bmatrix}},$where the one-way traffic components between each of the nodes 12 a, 12b, and 12 c equal 100 Mbps, then the growth factor α_(max)=1.5 based onthe capacity matrix C specifying link capacities of 150 Mbps. Hence, thecapacity C_(T2) for the traffic distribution T2 would be C_(T2)=900Mbps. Hence, C_(T) can be determined by:

${C_{T} = {\sum\limits_{i,j}T^{\prime}}},{{{where}\mspace{14mu} T^{\prime}} = {\alpha_{\max}{T.}}}$

A formal definition used to determine C_(T) using linear programmingresources is described in the attached Appendix B.

Hence, if the absolute capacity (C_(A)) is greater than the capacity forthe given traffic distribution (C_(T)), a network engineer can concludethat the network topology is not a good match for the trafficdistribution because C_(T)/C_(A), is substantially less than one (i.e.,C_(T) is not substantially equal to C_(A)). In the network 10illustrated in FIGS. 1A, 1B, and 1C it becomes apparent that the networktopology is not a good match to the traffic distribution because alltraffic is between nodes 20 12 a (“1”) and 12 c (“3”); in fact, thenetwork design can be improved by removing the node 12 b (“2”)and addingcapacity between the nodes 12 a, 12 d, and 12 c.

An additional capacity metric is capacity for a given trafficdistribution and set of routing constraints (C_(TR)): the networkcapacity for a given traffic distribution and routing constraints isdefined as the maximum throughput of a network 10 for a given trafficdistribution and a set of routing rules. The network capacity for agiven traffic distribution and routing constraints (C_(TR)) iscalculated by determining the amount that traffic can grow whileconforming to a given traffic distribution, in accordance with a givenset of routing constraints, without exceeding the maximum allowableutilization (i.e., capacity) on any link 16. For example, routingconstraints that may be applied to the traffic distribution of FIG. 1Cmay include that each traffic element in the traffic matrix T is splitinto two equal components that are routed over disjoint links; anotherexemplary routing constraint may include requiring the path length to besmaller than a specified value, or requiring multiple paths between thesame source/destination nodes that are disjoint.

Hence, three capacity metrics can be used to determine and evaluatetraffic distribution and applied routing constraints relative to thenetwork topology: if C_(T) is substantially equal to C_(A) such thatC_(T)/C_(A)≈1, then there is a good match (i.e., optimization) betweenthe network design and the given traffic distribution; however, if C_(T)is substantially less than C_(A) such that C_(T)/C_(A)<<1, then thenetwork topology is not a good match (i.e., not optimized) for thetraffic distribution.

Similarly, the capacity for given traffic distribution and set ofrouting rules (C_(TR)) can be compared with the absolute capacity(C_(A)) to determine whether the prescribed routing constraints areoptimized relative to the prescribed network topology: if C_(TR) issubstantially equal to C_(A) such that C_(TR)/C_(T)≈1, then there is agood match (i.e., optimization) between the network design and therouting constraints for the given traffic distribution; however, ifC_(TR) is substantially less than C_(A) such that C_(T)/C_(A)<<1, thenthe network topology is not a good match (i.e., not optimized) forrouting constraints relative to the traffic distribution.

As described below, additional metrics are available to quantify therelative optimization between the network topology, the trafficdistribution, and the routing constraints. As described below, theadditional metrics include available capacity of a network (AC), networkcapacity utilization (ρ), and capacity utilization efficiency (μ).

FIG. 2 is a diagram illustrating a computer-based system 30 configuredfor evaluating the network 10 based on determining prescribed networkmetrics including absolute capacity (C_(A)), capacity for a giventraffic distribution (C_(T)), and capacity for a given trafficdistribution and prescribed routing constraints (C_(TR)), according toan embodiment of the present invention. In particular, the system 30includes multiple executable resources configured for identifyingnetwork attributes, calculating network metrics, and/or evaluating thenetwork attributes and metrics based on linear programming resources.

The system 30 includes commercially available linear programmingresources 32 including a mixed integer program resource 34. An exampleof a commercially available linear programming resource 32 is the ILOGOptimization Suite from ILOG, Inc., Mountain View, Calif. The linearprogramming resources 32 are configured for minimizing a linearobjective function of continuous real variables, subject to linearconstraints. As described below, routing constraints may be specified interms of either binary and/or continuous variables, depending onapplication, hence the mixed integer program resource 34 is utilized tominimize a linear objective function of continuous real variables andbinary variables, subject to linear constraints.

As illustrated in FIG. 2, the system 30 is configured for storingnetwork attributes that may be predetermined based on network topologydefinitions or determined from measurements over time (e.g., averagetraffic distribution over one week). In particular, the system 30includes a capacity resource 36 configured for storing the link capacitymatrix C, a traffic resource 38 configured for storing the trafficdistribution matrix T, and a routing constraints resource 40 configuredfor storing identified routing constraints, for example trafficallocation variables (X) 40 a, link use indicators (I) 40 b, etc.Additional details related to the routing constraints will be describedbelow.

The system 30 also includes resources 42 configured for storingcalculated parameters used during linear programming, for example thetraffic growth factor (α) 42 a and the maximum traffic growth factor(α_(max)) 42 b for the corresponding traffic distribution T. Each of theresources configured for storing values may be implemented in software,using software based registers under control of executable code.

The system 30 also includes resources 44 configured for calculatingrespective network metrics based on applying the linear programmingresources 32 or the mixed integer program resource 34. In particular,the resources 44 a, 44 b, and 44 c are configured for calculatingabsolute capacity (C_(A)), capacity for a given traffic distribution(C_(T)), and capacity for a given traffic distribution and prescribedrouting constraints (C_(TR)), respectively. The resources 44 d, 44 e,and 44 f are configured for calculating available absolute capacity(AC_(A)), available capacity for a given traffic distribution (AC_(T)),and available capacity for a given traffic distribution and prescribedrouting constraints (AC_(TR)), respectively. The available capacity iscalculated by using the same methods to calculate total capacity (e.g.,C_(A), C_(T), C_(TR)) after subtracting existing traffic load on eachlink from the total link capacity.

The resources 44 g, 44 h, and 44 i are configured for calculatingnetwork capacity utilization relative to absolute capacity (ρ_(A)=Totaltraffic carried/C_(A)), network capacity utilization relative to a giventraffic distribution (ρ_(T)=Total traffic carried/C_(T)), and networkcapacity utilization relative to a given traffic distribution androuting constraints (ρ_(TR)=Total traffic carried/C_(TR)), respectively.

The resources 44 j, 44 k, and 44 l are configured for calculatingnetwork capacity utilization efficiency relative to absolute capacity(μ_(A)=Total traffic carried/(C_(A)−AC_(A))), network capacityutilization efficiency relative to capacity for a given trafficdistribution (μ_(T)=Total traffic carried/(C_(T)−AC_(T))), and networkcapacity utilization efficiency relative to capacity for a given trafficdistribution and prescribed routing constraints (μ_(TR)=Total trafficcarried/(C_(TR)−AC_(TR))), respectively.

Hence, the resources 44 use the linear programming resources calculatethe respective metrics based on the linear programming resources 32,where the resources 44 c, 44 f, 44 i, and 44 l, configured forcalculating the respective metrics based on routing constraints that maybe specified in terms of either binary and/or continuous variables,utilize the mixed integer program resource 34 to minimize a linearobjective function of continuous real variables and binary variables,subject to linear constraints.

The system 30 also includes evaluation processes 46 configured forproviding quantitative analysis of whether a network topology, trafficdistribution, and routing constraints are optimized relative to eachother. For example, the traffic distribution evaluation process 46 a isconfigured for determining whether the traffic distribution is optimizedrelative to the prescribed network topology: if C_(T)/C_(A)≈1, thenthere is optimization between the network topology and the given trafficdistribution; however if C_(T)/C_(A)<<1, then the network topology andgiven traffic distribution are not optimized relative to each other.

The routing constraints evaluation process 46 b is configured fordetermining whether the network topology, the given trafficdistribution, and the prescribed set of routing constraints areoptimized relative to each other: if C_(TR)/C_(A)≈1, then there isoptimization between the network topology, the given trafficdistribution, and the prescribed routing constraints; however ifC_(TR)/C_(A)<<1, then the network topology, the given trafficdistribution and the routing constraints are not optimized relative toeach other.

The traffic growth/utilization evaluation process 46 c is configured foridentifying whether there is room for traffic growth, and whether thenetwork is being utilized in the best way possible: if ρ_(T)≈1 andρ_(A)<<1, there is no room for traffic growth for the given trafficdistribution, however the network has not been used in the best waypossible (i.e., network topology and traffic distribution have not beenoptimized relative to each other); if ρ_(T)≈1 and ρ_(A)≈1, there is agood match between the network design and the given trafficdistribution, there is no room for traffic growth, and the network isbeing utilized in the best way possible (i.e., optimized) for the giventraffic distribution. Similarly, if ρ_(TR)≈1 and ρ_(A)<<1, the networkis operating at its full capacity for the given traffic distribution andset of routing constraints, however the network is not been used (i.e.,not optimized) in the best way possible; if ρ_(TR)≈1 and ρ_(A)≈1, thereis a good match between the network design and the given trafficdistribution and set of routing rules, there is no room for trafficgrowth, and the network is being utilized in the best way possible forthe given traffic distribution and set of routing constraints.

The capacity utilization efficiency evaluation process 46 d isconfigured for determining the ratio of the volume of traffic carried bythe network to the network capacity used to carry the traffic: ifμ_(A)<1, then the absolute capacity (C_(A)) is not being utilizedefficiently; if μ_(T)<1, then the capacity (C_(T)) for the determinedtraffic distribution T is not being utilized efficiently (i.e., thenetwork is using more capacity (C_(T)) than necessary to serve theexisting traffic); if μ_(TR)<1, then the network is using more capacity(C_(TR)) than necessary to serve the existing traffic T according to theprescribed routing constraints.

Hence, the evaluation processes 46 enable a network engineer toquantitatively determine whether the traffic distribution T is optimizedrelative to the prescribed network topology, and whether the prescribedrouting constraints are optimized relative to the traffic distributionand the prescribed network topology. In addition, the trafficgrowth/utilization factors ρ determined by the process 46 c enable anetwork engineer to quantitatively identify whether the network isoptimized and whether there is additional room for traffic growth, andthe capacity utilization efficiency evaluation process 46 d enables thenetwork designer to determine from the capacity utilization efficiencyparameters μ whether the network is being utilized efficiently, orwhether capacity is being unnecessarily used to serve the existingtraffic.

FIG. 3 is a diagram illustrating the method of evaluating a network,according to an embodiment of the present invention. The steps describedin FIG. 3 can be implemented as executable code that is stored on acomputer readable medium (e.g., a hard disk drive, a floppy drive, arandom access memory, a read only memory, an EEPROM, a compact disk,etc), or propagated via a computer readable medium (e.g., a transmissionwire, an optical fiber, a wireless transmission medium utilizing anelectromagnetic carrier wave, etc.).

The method begins in step 50, where the input parameters includingcapacity (C) and traffic distribution (T) are obtained by the processes36 and 38, respectively. The capacity matrix C may be stored, forexample, by a network engineer during provisioning of the network 10,and the traffic distribution matrix T may be determined, for example,based on traffic measurements over a prescribed time interval (e.g., oneweek).

The prescribed routing constraints are stored in step 52 by the process40. In particular, the routing constraints are stored in step 52 interms of equations involving the traffic allocation variables (X) storedby the process 40 a, and the link use variables (I) stored by theprocess 40 b.

The traffic allocation variable X can described as follows: givenT_(k,l) as the traffic between source node k and destination l, andC_(i,j) as the capacity of the circuit between node i and node j, thenX_(ijkl) is the portion of traffic from node k to node l placed on thecircuit between nodes i and j. Hence, a link capacity constraint (usedfor the capacity definitions) may be written that limits traffic to thelink capacity as follows: given the network 10′ of FIG. 4 havingcapacity C and traffic distribution T, where

$C = {{\begin{bmatrix}0 & 150 & 0 & 150 & 150 \\150 & 0 & 150 & 0 & 150 \\0 & 150 & 0 & 150 & 0 \\150 & 0 & 150 & 0 & 150 \\150 & 150 & 0 & 150 & 0\end{bmatrix}\mspace{20mu}{and}\mspace{14mu} T} = \begin{bmatrix}0 & 100 & 10 & 20 \\10 & 0 & 50 & 30 \\10 & 10 & 0 & 20 \\100 & 50 & 75 & 0\end{bmatrix}}$then limit traffic not to exceed link capacity C₁₅=150 Mbps (from node12 e (“1”) to node 12 i (“5”)) based on the constraint:

${{\sum\limits_{k = 1}^{4}\;{\sum\limits_{l = 1}^{4}\; X_{15k\; l}}} < C_{15}} = {150\mspace{14mu}{{Mbps}.}}$In other words, the link 22 a from node 12 e to node 12 i can carrytraffic between any of the node pairs, so long as the total trafficbetween the different source node pairs passing through the link 12 ishould be less than the link capacity. Similar constraints can bewritten for all the links.

Routing constraints such as limits on path delay and number of hopsalong a path can be written in terms of link use indicators I_(ijklm).Link use indicators are defined as follows:I_(ijklm)=1 if traffic k, l, m is using link i,j; 0 if traffic k, l, mis not using link i, j.For example, for the network of FIG. 4, the routing constraint thatrequires all paths from node 12 e (“1”) to node 12 f (“2”) to be lessthan four (4) links can be written as follows:

${\sum\limits_{i,j}I_{i\;{jl2m}}} < 4$(i.e., path m from node 1 to node 2 should be less than 4 links where mis the path number).If only two paths are required (m=1, m=2), then depending on the otherconstraints, path 1 may go directly from 1 to 2 (nodes 12 e and 12 f)and path 2 may go through node 5 (nodes 12 e, 12 i, and 12 f). In thiscase, the link use indicators I₁₂₁₂₁, I₁₅₁₂₂, and I₅₂₁₂₂ would be equalto 1 and all others (e.g., I₁₄₁₂₁, I₃₂₁₂₁, I₅₄₁₂₂) would be equal to 0.

Hence, the required constraints can be written as linear equationsinvolving traffic allocation variables X, or binary use indicators I, ora combination thereof.

Once all constraints have been stored by the resource 40, the absolutecapacity computation resource 44 a utilizes the linear programmingresource 32 to determine in step 54 the absolute network capacity C_(A).An exemplary formal definition for finding a solution for the absolutenetwork capacity (C_(A)) is illustrated in the attached Appendix A. Thecapacity computation resource 44 b also utilizes the linear programmingresource 32 in step 56 to determine the network capacity (C_(T)) for thegiven traffic distribution T. An exemplary formal definition for findinga solution for the network capacity C_(T) is illustrated in the attachedAppendix B. The capacity computation resource 44 c utilizes in step 58the mixed integer program resource 34 to determine the capacity (C_(TR))for the determined traffic distribution T and the prescribed routingconstraints X and I. An exemplary formal definition for finding asolution for the capacity (C_(TR)) is illustrated in the attachedAppendix C. Available capacity (AC) also is calculated by the resources44 d, 44 e, and 44 f.

The computation resources 44 g, 44 h, and 44 i generate in step 60 therespective network capacity utilization metrics ρ_(A), ρ_(T), andρ_(TR), respectively, and the computation resources 44 j, 44 k, and 44 lgenerate in step 62 the respective capacity utilization efficiencymetrics μ_(A), μ_(T) and μ_(TR), respectively.

Once the network capacity, utilization, and efficiency metrics have beengenerated by the competition resources 44, the evaluation resources 46evaluate in step 64 the traffic distribution, routing constraints,traffic growth/utilization, and capacity utilization efficiency, asdescribed above with respect to FIG. 2.

According to the disclosed embodiment, network traffic and performancecan be precisely analyzed based on systematic definitions of capacity,utilization, and efficiency metrics under different constraints. Theestablishment of recognized metrics enables systematic analysis for anynetwork, enabling comparison of different networks, as well asquantifying changes in traffic distribution, routing rules, or networkcapacity. Finally, the aforementioned metrics can be used effectively tocharacterize a network and a network state accurately and in a concisemanner. The disclosed methodology can be applied to different networktechnologies, including Internet Protocol networks, ATM networks, framerelay networks, etc.

While this invention has been described in connection with what ispresently considered to be the most practical and preferred embodiment,it is to be understood that the invention is not limited to thedisclosed embodiments, but, on the contrary, is intended to covervarious modifications and equivalent arrangements included within thespirit and scope of the appended claims.

1. A method of evaluating a network having origination/destination nodesand interconnecting links arranged according to a prescribed networktopology, the method including: determining an absolute capacity of thenetwork, based on capacity attributes for the respective links, as amaximum traffic volume that can be carried by the network according tothe prescribed network topology independent of traffic distribution ofthe network and routing constraints of the network; determining a secondnetwork capacity of the network, based on the capacity attributes of therespective links, as a maximum traffic volume that can be carried by thenetwork according to the prescribed network topology and the trafficdistribution of the network, independent of the routing constraints; andoutputting information to a user, based on the absolute capacity and thesecond network capacity, that provides an evaluation of the trafficdistribution of the network.
 2. The method of claim 1, furthercomprising: determining a third network capacity based on the capacityattributes, as a maximum traffic volume that can be carried by thenetwork according to the prescribed network topology, the trafficdistribution and the routing constraints; wherein the outputting theinformation includes outputting second information used to evaluate,based on the absolute capacity and the third network capacity, therouting constraints relative to the network topology and the trafficdistribution.
 3. The method of claim 2, wherein the outputting thesecond information further includes identifying whether the routingconstraints are optimized relative to the network topology and thetraffic distribution, based on whether the third network capacity issubstantially equal to the absolute capacity.
 4. The method of claim 2,wherein the determining the third network capacity includes applying anexecutable mixed integer program resource using the capacity attributes,the determined traffic distribution, and the routing constraints toobtain the third network capacity.
 5. The method of claim 2, furthercomprising: determining a first network capacity utilization relative tothe absolute capacity based on the traffic distribution; determining athird network capacity utilization relative to the third networkcapacity based on the traffic distribution; and outputting thirdinformation, based on the first network capacity utilization and thethird network capacity utilization, to evaluate traffic growthavailability relative to the prescribed network topology, the trafficdistribution and the routing constraints.
 6. The method of claim 5,further comprising: determining a capacity utilization efficiency for atleast one of the absolute capacity, the second network capacity, and thethird network capacity, based on the traffic distribution.
 7. The methodof claim 1, wherein the outputting information includes identifyingwhether the traffic distribution is optimized relative to the networktopology based on whether the second network capacity is substantiallyequal to the absolute capacity.
 8. The method of claim 1, wherein: thedetermining the absolute capacity includes applying an executable linearprogramming resource using the capacity attributes to obtain theabsolute network capacity; and the determining the second networkcapacity includes applying the executable linear programming resourceusing the capacity attributes and the traffic distribution to obtain thesecond network capacity.
 9. The method of claim 1, further comprising:determining a first network capacity utilization relative to theabsolute capacity based on the traffic distribution; determining asecond network capacity utilization relative to the second networkcapacity based on the traffic distribution; and outputting secondinformation, based on the first network capacity utilization and thesecond network capacity utilization, to evaluate traffic growthavailability relative to the network topology and the trafficdistribution.
 10. The method of claim 9, further comprising determininga capacity utilization efficiency for at least one of the absolutecapacity, and the second network capacity, based on the trafficdistribution.
 11. A system configured for evaluating a network havingorigination/destination nodes and interconnecting links arrangedaccording to a prescribed network topology, the system including: meansfor determining an absolute capacity of the network, based on capacityattributes for the respective links, as a maximum traffic volume thatcan be carried by the network according to the prescribed networktopology independent of a traffic distribution of the network and therouting constraints of the network; means for determining a secondnetwork capacity of the network based on the capacity attributes of therespective links, as a maximum traffic volume that can be carried by thenetwork according to the prescribed network topology and the trafficdistribution of the network, independent of the routing constraints; andmeans for outputting information to a user, based on the absolutecapacity and the second network capacity, that provides an evaluation ofthe traffic distribution of the network.
 12. The system of claim 11,further comprising: means for determining a third network capacity basedon the capacity attributes, as a maximum traffic volume that can becarried by the network according to the prescribed network topology, thetraffic distribution and the routing constraints; wherein the means foroutputting the information outputs second information used to evaluate,based on the absolute capacity and the third network capacity, therouting constraints relative to the network topology and the trafficdistribution.
 13. The system of claim 12, wherein the means foroutputting the second information is configured to identify whether therouting constraints are optimized relative to the network topology andthe traffic distribution, based on whether the third network capacity issubstantially equal to the absolute capacity.
 14. The system of claim12, wherein the means for determining the third network capacity isconfigured to apply an executable mixed integer program resource usingthe capacity attributes, the determined traffic distribution, and therouting constraints to obtain the third network capacity.
 15. The systemof claim 12, further comprising: means for determining a first networkcapacity utilization relative to the absolute capacity based on trafficdistribution; means for determining a third network capacity utilizationrelative to the third network capacity based on the trafficdistribution; and means for outputting third information, based on thefirst network capacity utilization and the third network capacityutilization, to evaluate traffic growth availability relative to theprescribed network topology, the traffic distribution and the routingconstraints.
 16. The system of claim 15, further comprising means fordetermining a capacity utilization efficiency for at least one of theabsolute capacity, the second network capacity, and the third networkcapacity, based on the traffic distribution.
 17. The system of claim 11,wherein the means for outputting information is configured to identifywhether the traffic distribution is optimized relative to the networktopology based on whether the second network capacity is substantiallyequal to the absolute capacity.
 18. The system of claim 11, wherein: themeans for determining the absolute capacity is configured to apply anexecutable linear programming resource using the capacity attributes toobtain the absolute network capacity; and the means for determining thesecond network capacity is configured to apply the executable linearprogramming resource using the capacity attributes to obtain the secondnetwork capacity.
 19. The system of claim 11, further comprising: meansfor determining a first network capacity utilization relative to theabsolute capacity based on the traffic distribution; means fordetermining a second network capacity utilization relative to the secondnetwork capacity based on the traffic distribution; and means foroutputting second information, based on the first network capacityutilization and the second network capacity utilization, to evaluatetraffic growth availability relative to the network topology and thetraffic distribution.
 20. The system of claim 19, further comprisingmeans for determining a capacity utilization efficiency for at least oneof the absolute capacity, and the second network capacity, based on thetraffic distribution.
 21. A computer readable medium having storedthereon sequences of instructions for evaluating a network havingorigination/destination nodes and interconnecting links arrangedaccording to a prescribed network topology, the sequences ofinstructions including instructions for: determining an absolutecapacity of the network, based on capacity attributes for the respectivelinks, as a maximum traffic volume that can be carried by the networkaccording to the prescribed network topology independent of trafficdistribution of the network and routing constraints of the network;determining a second network capacity of the network, based on thecapacity attributes of the respective links, as a maximum traffic volumethat can be carried by the network according to the prescribed networktopology and the traffic distribution of the network, independent of therouting constraints; and outputting information to a user, based on theabsolute capacity and the second network capacity, that provides for anevaluation of the traffic distribution of the network.
 22. The medium ofclaim 21, further comprising instructions for: determining a thirdnetwork capacity based on the capacity attributes, as a maximum trafficvolume that can be carried by the network according to the prescribednetwork topology, the traffic distribution and the routing constraints;wherein the outputting the information includes outputting secondinformation used to evaluate, based on the absolute capacity and thethird network capacity, the routing constraints relative to the networktopology and the traffic distribution.
 23. The medium of claim 22,wherein the outputting the second information includes identifyingwhether the routing constraints are optimized relative to the networktopology and the traffic distribution, based on whether the thirdnetwork capacity is substantially equal to the absolute capacity. 24.The medium of claim 22, wherein the determining the third networkcapacity includes applying an executable mixed integer program resourceusing the capacity attributes, the determined traffic distribution, andthe routing constraints to obtain the third network capacity.
 25. Themedium of claim 22, further comprising instructions for: determining afirst network capacity utilization relative to the absolute capacitybased on the traffic distribution; determining a third network capacityutilization relative to the third network capacity based on the trafficdistribution; and outputting third information, based on the firstnetwork capacity utilization and the third network capacity utilization,to evaluate traffic growth availability relative to the prescribednetwork topology, the traffic distribution and the routing constraints.26. The medium of claim 25, further comprising instructions fordetermining a capacity utilization efficiency for at least one of theabsolute capacity, the second network capacity, and the third networkcapacity, based on the traffic distribution.
 27. The medium of claim 21,wherein the outputting information includes identifying whether thetraffic distribution is optimized relative to the network topology basedon whether the second network capacity is substantially equal to theabsolute capacity.
 28. The medium of claim 21, wherein: determining theabsolute capacity includes applying an executable linear programmingresource using the capacity attributes to obtain the absolute networkcapacity; and determining the second network capacity includes applyingthe executable linear programming resource using the capacity attributesand the traffic distribution to obtain the second network capacity. 29.The medium of claim 21, further comprising instructions for: determininga first network capacity utilization relative to the absolute capacitybased on the traffic distribution; determining a second network capacityutilization relative to the second network capacity based on the trafficdistribution; and outputting second information, based on the firstnetwork capacity utilization and the second network capacityutilization, to evaluate traffic growth availability relative to thenetwork topology and the traffic distribution.
 30. The medium of claim29, further comprising instructions for: determining a capacityutilization efficiency for at least one of the absolute capacity, andthe second network capacity, based on the traffic distribution.
 31. Amethod of evaluating a network having origination/destination nodes andinterconnecting links arranged according to a predetermined networktopology, the method including: determining an absolute capacity of thenetwork based on capacity attributes for the respective links, theabsolute capacity identifying a maximum traffic volume that can becarried by the network according to a prescribed network topology of thenetwork; determining a second network capacity based on the capacityattributes and a determined traffic distribution of the network betweenthe respective origination/destination nodes, the second networkcapacity identifying a maximum traffic volume that can be carried by thenetwork according to the prescribed network topology and the determinedtraffic distribution; and outputting information to a user, based on theabsolute capacity and the second network capacity, that provides anevaluation of the determined traffic distribution relative to theprescribed network topology.
 32. The method of claim 31, furthercomprising: determining a third network capacity based on the capacityattributes, the determined traffic distribution and the prescribedrouting constraints, the third network capacity identifying a maximumtraffic volume that can be carried by the network according to theprescribed network topology, the determined traffic distribution and theprescribed routing constraints; wherein the outputting informationincludes outputting second information used for evaluation of theprescribed routing constraints relative to the prescribed networktopology and the determined traffic distribution based on the absolutecapacity and the third network capacity.
 33. A method of evaluatingperformance of a network, having origination/destination nodes andinterconnecting links arranged according to a prescribed networktopology, the method including: determining an absolute capacity of thenetwork, based on capacity attributes for the respective links, as amaximum traffic volume that can be carried by the network according tothe prescribed network topology independent of traffic distribution ofthe network and routing constraints of the network; determining a secondnetwork capacity of the network, based on the capacity attributes of therespective links as a maximum traffic volume that can be carried by thenetwork according to the prescribed network topology and the trafficdistribution, independent of the routing constraints; and outputtinginformation to a user, based on the absolute capacity and the secondnetwork capacity, that provides an evaluation of whether the prescribednetwork topology and the determined traffic distribution are optimizedrelative to each other within the network.
 34. The method of claim 33,further comprising: determining a third network capacity based on thecapacity attributes, as a maximum traffic volume that can be carried bythe network according to the prescribed network topology, the trafficdistribution and the routing constraints; wherein the outputting theinformation includes outputting second information used to evaluate,based on the absolute capacity and the third network capacity, theprescribed routing constraints relative to the prescribed networktopology and the determined traffic distribution.